Remote Viewer Input for Autonomous Threat Response
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Solution Overview
Problem
Autonomous vehicles face limitations in detecting and responding to threats, such as obscured objects or complex scenarios, due to their reliance on onboard sensors, which can lead to inadequate decision-making and potential accidents.
Innovation Solution
The integration of remote viewer input, where external sources provide additional data and recommendations to the vehicle's AI, augmenting its decision-making capabilities by presenting environmental constructs and soliciting driving strategies from multiple remote viewers to enhance response accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles rely solely on onboard sensors and local decision-making, then device complexity is reduced, but detection precision and decision-making reliability deteriorate in complex scenarios
Solution Approach 1:
The patent introduces remote viewers as intermediary human operators who assist the autonomous vehicle's AI system. When the AI detects a threat or uncertain situation, it solicits recommendations from remote viewers who provide additional perspective and decision-making support, thereby improving detection precision without requiring the vehicle itself to have enhanced sensors or processing power.
Solution Approach 2:
The system transitions from purely local, onboard decision-making to a distributed architecture that incorporates remote human expertise. This adds a spatial dimension to the decision-making process, allowing the vehicle to leverage external brains and perspectives that exist in a different physical location, effectively expanding the system's cognitive capabilities beyond the vehicle's physical boundaries.
2Reliability
If autonomous vehicles use only local AI decision-making, then response speed is maintained, but decision-making reliability deteriorates in ambiguous situations
Solution Approach 1:
The system prepares remote viewers in advance by providing them with contextual information about the vehicle's environment and potential threats before they are needed. This preliminary briefing allows remote viewers to be mentally prepared and ready to provide recommendations quickly when the AI solicits their input during critical moments.
Solution Approach 2:
The system implements a feedback loop where the AI continuously monitors the environment, identifies threats or uncertain situations, solicits recommendations from remote viewers, and then incorporates this feedback into its decision-making process. This closed-loop feedback mechanism improves reliability by validating AI decisions against human expertise while maintaining responsive timing through iterative refinement.
3Measurement precision
If the vehicle solicits recommendations from multiple remote viewers, then decision accuracy is improved, but communication overhead and system complexity increase
Solution Approach 1:
The system extracts and transmits only the essential environmental data and threat information needed for remote viewers to provide recommendations, rather than transmitting complete sensor datasets. This selective extraction minimizes communication overhead while providing remote viewers with sufficient information to make accurate assessments.
Solution Approach 2:
The system transforms complex sensor data into simplified parameters and metrics that are relevant to remote viewers' decision-making needs. By changing the representation of environmental data from raw sensor readings to meaningful contextual parameters, the system reduces communication bandwidth requirements while maintaining decision accuracy.
Data Source
AI summary
Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.